Thanks a lot for this second report, and congratulations for your hard work on this exercise. How long did it take you? I assume you spent a significant amount of time with the docker environment, didn't you?

This time I have plenty of things to comment on!

Before starting MadGraph, I picked apart the acronyms in the name of the package: 'MadGraph5_aMC@NLO'. Specifically, I wanted to know what the 'aMC' and 'NLO' terms meant. After some searching, here's what I concluded:
aMC: adjoint Monte Carlo.
NLO: Next-to-Leading-Order.

The name comes from the merging of the code MadGraph5 (Mad refers to Madison in the US, and Graph to Feynman diagrams or graphs) and MC@NLO (a Monte Carlo event generator achieving predictions at next-to-leading order accuracy in the strong coupling; more information on this are provided in the 6^th episode and the upcoming 7^th episode). The extra a in the name refers to automation (MadGraph5 was an automated package for predictions at the leading-order accuracy; MC@NLO was not automated) .

By automation, I mean that it is sufficient to specify the process of interest and the physics model, and the code does the rest.

Note the presence of '#' at the end of the MG5 commands above. Through trial and error, I found that '#' indicates a comment in MadGraph's REPL. For example, generate p p > t t~ # Collider process is a valid command.

That’s right. This follows standard Python conventions: anything at the right of the hash is ignored.

An aside on filesystems
Initially I ran the simulation on a shared filesystem (/host/c). That was slow. So I tried both a container-local filesystem and a filesystem mounted in RAM (ramfs). For comparison, I measured the elapsed time of the 'launch' command (minus menu interactions).

That’s interesting. I don’t use dockers (as I run everything locally), so that I am unable to really comment on this. While RAM filesystems seems better, I assume there are limited in disk space, aren’t they? In this case, this may be a weakness.

By the way, why don't you run everything locally (possibly in a virtual environment)?

Since I'm running MadGraph in a container, there is no interactive display session. It's (nearly) headless. So I'm forced to copy graphical results (like images) to the host machine before viewing. That does incur some overhead.

So you don’t have access to an interactive terminal? That’s definitely an overhead as this makes you unable to read error messages and capture them live (if relevant). So you use it more like a cluster on which you would submit a job and recover the output files after they are transferred locally, don’t you? This makes life complicated for testing purpose...

Once again, congratulations for having achieved this episode!

Cheers!